#importing modules
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

data = pd.read_csv("datasets.csv", encoding_errors="ignore")

data.head()

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20770 entries, 0 to 20769
Data columns (total 22 columns):
 #   Column                          Non-Null Count  Dtype  
---  ------                          --------------  -----  
 0   id                              20770 non-null  float64
 1   name                            20770 non-null  object 
 2   host_id                         20770 non-null  int64  
 3   host_name                       20770 non-null  object 
 4   neighbourhood_group             20770 non-null  object 
 5   neighbourhood                   20763 non-null  object 
 6   latitude                        20763 non-null  float64
 7   longitude                       20763 non-null  float64
 8   room_type                       20763 non-null  object 
 9   price                           20736 non-null  float64
 10  minimum_nights                  20763 non-null  float64
 11  number_of_reviews               20763 non-null  float64
 12  last_review                     20763 non-null  object 
 13  reviews_per_month               20763 non-null  float64
 14  calculated_host_listings_count  20763 non-null  float64
 15  availability_365                20763 non-null  float64
 16  number_of_reviews_ltm           20763 non-null  float64
 17  license                         20770 non-null  object 
 18  rating                          20770 non-null  object 
 19  bedrooms                        20770 non-null  object 
 20  beds                            20770 non-null  int64  
 21  baths                           20770 non-null  object 
dtypes: float64(10), int64(2), object(10)
memory usage: 3.5+ MB

data.shape

(20770, 22)

data.describe()

#Data cleaning
data.isnull().sum()

id                                 0
name                               0
host_id                            0
host_name                          0
neighbourhood_group                0
neighbourhood                      7
latitude                           7
longitude                          7
room_type                          7
price                             34
minimum_nights                     7
number_of_reviews                  7
last_review                        7
reviews_per_month                  7
calculated_host_listings_count     7
availability_365                   7
number_of_reviews_ltm              7
license                            0
rating                             0
bedrooms                           0
beds                               0
baths                              0
dtype: int64

data[pd.isnull(data['price'])].head(2)

data['price'].fillna(10, inplace=True)

C:\Users\welcome\AppData\Local\Temp\ipykernel_8952\3963316189.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  data['price'].fillna(10, inplace=True)

#dropping All missing raws and column
data.dropna(inplace=True)
data.isnull().sum()

id                                0
name                              0
host_id                           0
host_name                         0
neighbourhood_group               0
neighbourhood                     0
latitude                          0
longitude                         0
room_type                         0
price                             0
minimum_nights                    0
number_of_reviews                 0
last_review                       0
reviews_per_month                 0
calculated_host_listings_count    0
availability_365                  0
number_of_reviews_ltm             0
license                           0
rating                            0
bedrooms                          0
beds                              0
baths                             0
dtype: int64

data.shape

(20763, 22)

#dealing with dublicvate
data.duplicated().sum()

12

data[data.duplicated()]

data.drop_duplicates(inplace=True)

data.duplicated().sum()

0

#price distribution
sns.histplot(data=data, x ="price")

<Axes: xlabel='price', ylabel='Count'>

#dealing with outliers
sns.boxenplot(data=data, x = "price")

<Axes: xlabel='price'>

Q1 = data['price'].quantile(0.25)
Q3 = data['price'].quantile(0.75)
IQR = Q3 - Q1

lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR

outliers = data[(data['price'] < lower_bound) | (data['price'] > upper_bound)]
print(outliers)

                 id                                               name  \
20     2.992360e+07  Serviced apartment in New York City · Studio ·...   
22     3.471850e+07  Condo in New York · 2 bedrooms · 2 beds · 2 baths   
52     1.027808e+06  Loft in New York · ★4.87 · 2 bedrooms · 1 bed ...   
54     1.146653e+06  Condo in New York · ★4.90 · 2 bedrooms · 1 bed...   
69     8.070000e+17  Home in Queens · ★4.50 · 6 bedrooms · 8 beds ·...   
...             ...                                                ...   
20669  4.023946e+07  Townhouse in Brooklyn · ★4.50 · 2 bedrooms · 2...   
20687  3.100156e+07  Boutique hotel in New York · ★5.0 · 1 bedroom ...   
20703  2.720010e+07  Rental unit in Brooklyn · ★4.84 · 2 bedrooms ·...   
20723  4.550539e+07  Rental unit in New York · ★5.0 · 2 bedrooms · ...   
20732  1.336161e+07  Rental unit in New York · ★4.89 · 2 bedrooms ·...   

         host_id          host_name neighbourhood_group       neighbourhood  \
20     220229838      Chamber Hotel           Manhattan             Midtown   
22       6674394                Eri           Manhattan     Upper West Side   
52       5655889  East Village Loft           Manhattan        East Village   
54        836168              Henry           Manhattan     Upper West Side   
69     496749638                  B              Queens    South Ozone Park   
...          ...                ...                 ...                 ...   
20669    3591955            Charles            Brooklyn  Bedford-Stuyvesant   
20687   26556695  Justin And Alyssa           Manhattan             Midtown   
20703     416361                  O            Brooklyn  Bedford-Stuyvesant   
20723   25596933               Anna           Manhattan             Tribeca   
20732    8961407              Jamie           Manhattan              Harlem   

        latitude  longitude        room_type   price  ...  last_review  \
20     40.761760 -73.976590       Hotel room  1000.0  ...     02/07/19   
22     40.792830 -73.971900  Entire home/apt   425.0  ...     25/06/19   
52     40.721540 -73.981740  Entire home/apt   450.0  ...     05/11/23   
54     40.792960 -73.964990  Entire home/apt  1000.0  ...     27/01/16   
69     40.664932 -73.815531  Entire home/apt   900.0  ...     13/10/23   
...          ...        ...              ...     ...  ...          ...   
20669  40.685340 -73.934620  Entire home/apt   400.0  ...     17/04/23   
20687  40.762170 -73.973490     Private room  1150.0  ...     30/09/23   
20703  40.680390 -73.943680  Entire home/apt   395.0  ...     26/07/23   
20723  40.720640 -74.009350  Entire home/apt   700.0  ...     22/11/22   
20732  40.805700 -73.946250  Entire home/apt   397.0  ...     08/09/23   

       reviews_per_month calculated_host_listings_count  availability_365  \
20                  0.02                           10.0             363.0   
22                  0.02                            1.0             358.0   
52                  0.42                            1.0             355.0   
54                  0.18                           10.0             364.0   
69                  1.40                            2.0             365.0   
...                  ...                            ...               ...   
20669               0.14                            1.0              83.0   
20687               0.09                            6.0             365.0   
20703               0.65                            1.0             178.0   
20723               0.10                            1.0             155.0   
20732               1.08                            3.0             274.0   

       number_of_reviews_ltm     license     rating bedrooms beds baths  
20                       0.0      Exempt  No rating   Studio    2     1  
22                       0.0  No License  No rating        2    2     2  
52                       2.0  No License       4.87        2    1     2  
54                       0.0  No License        4.9        2    1     1  
69                      14.0  No License        4.5        6    8     4  
...                      ...         ...        ...      ...  ...   ...  
20669                    1.0  No License        4.5        2    2     2  
20687                    2.0      Exempt          5        1    1     1  
20703                    4.0  No License       4.84        2    2   1.5  
20723                    0.0  No License          5        2    2     2  
20732                    3.0  No License       4.89        2    2     1  

[1382 rows x 22 columns]

data = data[(data['price'] >= lower_bound) & (data['price'] <= upper_bound)]

sns.histplot(data=data, x ="price")

<Axes: xlabel='price', ylabel='Count'>

sns.boxenplot(data=data, x = "price")

<Axes: xlabel='price'>

data.groupby(by="neighbourhood_group")["price"].mean()

neighbourhood_group
Bronx            102.182013
Brooklyn         133.538136
Manhattan        155.673090
Queens           112.401144
Staten Island    110.576923
Name: price, dtype: float64

data["price per bed"]=data["price"]/data["beds"]
data.head()

data.groupby(by="neighbourhood_group")["price per bed"].mean()

neighbourhood_group
Bronx             72.611098
Brooklyn          92.446518
Manhattan        117.843694
Queens            74.353753
Staten Island     67.112188
Name: price per bed, dtype: float64

data.columns

Index(['id', 'name', 'host_id', 'host_name', 'neighbourhood_group',
       'neighbourhood', 'latitude', 'longitude', 'room_type', 'price',
       'minimum_nights', 'number_of_reviews', 'last_review',
       'reviews_per_month', 'calculated_host_listings_count',
       'availability_365', 'number_of_reviews_ltm', 'license', 'rating',
       'bedrooms', 'beds', 'baths', 'price per bed'],
      dtype='object')

sns.barplot(data=data, x = "neighbourhood_group", y = "price", hue="room_type")

<Axes: xlabel='neighbourhood_group', ylabel='price'>

#number of reviews and price dependency
sns.scatterplot(data=data, x = "number_of_reviews", y = "price", hue="neighbourhood_group")

<Axes: xlabel='number_of_reviews', ylabel='price'>

sns.pairplot(data=data, vars=["price", 'availability_365', 'price per bed'], hue="neighbourhood_group")

<seaborn.axisgrid.PairGrid at 0x2082e582f20>

data['last_review'] = pd.to_datetime(data['last_review'], errors='coerce')

data['review_month'] = data['last_review'].dt.month

monthly_price_trends = data.groupby('review_month')['price'].mean()

plt.figure(figsize=(10, 6))
monthly_price_trends.plot(kind='line', marker='o', color='blue')
plt.title('Average Price Across Months', fontsize=16)
plt.xlabel('Month', fontsize=14)
plt.ylabel('Average Price', fontsize=14)
plt.xticks(ticks=range(1, 13), labels=[
    'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 
    'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'
], fontsize=12)
plt.grid(visible=True, linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

C:\Users\welcome\AppData\Local\Temp\ipykernel_8952\711206002.py:2: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  data['last_review'] = pd.to_datetime(data['last_review'], errors='coerce')

# Create a new column categorizing hosts by the number of listings
data['listing_category'] = data['calculated_host_listings_count'].apply(
    lambda x: 'Single Listing' if x == 1 else 'Multiple Listings'
)

# Calculate average price for single and multiple listing hosts
price_by_listing_category = data.groupby('listing_category')['price'].mean()

import seaborn as sns
import matplotlib.pyplot as plt

plt.figure(figsize=(8, 6))
sns.barplot(x=price_by_listing_category.index, y=price_by_listing_category.values, palette='viridis')
plt.title('Average Price: Single vs. Multiple Listings', fontsize=16)
plt.xlabel('Host Listing Category', fontsize=14)
plt.ylabel('Average Price', fontsize=14)
plt.grid(visible=True, linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

C:\Users\welcome\AppData\Local\Temp\ipykernel_8952\3278957082.py:13: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=price_by_listing_category.index, y=price_by_listing_category.values, palette='viridis')

!pip install folium

Collecting folium
  Downloading folium-0.18.0-py2.py3-none-any.whl (108 kB)
     ------------------------------------ 108.9/108.9 KB 524.7 kB/s eta 0:00:00
Requirement already satisfied: numpy in c:\users\welcome\appdata\local\programs\python\python310\lib\site-packages (from folium) (1.26.4)
Collecting branca>=0.6.0
  Downloading branca-0.8.0-py3-none-any.whl (25 kB)
Collecting xyzservices
  Downloading xyzservices-2024.9.0-py3-none-any.whl (85 kB)
     ---------------------------------------- 85.1/85.1 KB 1.6 MB/s eta 0:00:00
Requirement already satisfied: requests in c:\users\welcome\appdata\local\programs\python\python310\lib\site-packages (from folium) (2.31.0)
Requirement already satisfied: jinja2>=2.9 in c:\users\welcome\appdata\local\programs\python\python310\lib\site-packages (from folium) (3.1.4)
Requirement already satisfied: MarkupSafe>=2.0 in c:\users\welcome\appdata\local\programs\python\python310\lib\site-packages (from jinja2>=2.9->folium) (2.1.5)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\welcome\appdata\local\programs\python\python310\lib\site-packages (from requests->folium) (2024.2.2)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\users\welcome\appdata\local\programs\python\python310\lib\site-packages (from requests->folium) (3.3.2)
Requirement already satisfied: urllib3<3,>=1.21.1 in c:\users\welcome\appdata\local\programs\python\python310\lib\site-packages (from requests->folium) (2.2.1)
Requirement already satisfied: idna<4,>=2.5 in c:\users\welcome\appdata\local\programs\python\python310\lib\site-packages (from requests->folium) (3.6)
Installing collected packages: xyzservices, branca, folium
Successfully installed branca-0.8.0 folium-0.18.0 xyzservices-2024.9.0

WARNING: You are using pip version 22.0.4; however, version 24.3.1 is available.
You should consider upgrading via the 'C:\Users\welcome\AppData\Local\Programs\Python\Python310\python.exe -m pip install --upgrade pip' command.

import folium
from folium.plugins import MarkerCluster

# Create a base map centered around the average latitude and longitude
center_lat, center_lon = data['latitude'].mean(), data['longitude'].mean()
map_airbnb = folium.Map(location=[center_lat, center_lon], zoom_start=12)

# Add a MarkerCluster layer to group close points together
marker_cluster = MarkerCluster().add_to(map_airbnb)

# Iterate through the data and add markers to the map
for _, row in data.iterrows():
    folium.Marker(
        location=[row['latitude'], row['longitude']],
        popup=f"Price: ${row['price']}<br>Room Type: {row['room_type']}<br>Neighbourhood: {row['neighbourhood']}",
        tooltip=row['name']
    ).add_to(marker_cluster)

# Display the map
map_airbnb

# Define categories based on price
def customer_segment(row):
    if row['price'] < 100:
        return 'Budget-Conscious'
    elif 100 <= row['price'] <= 300:
        return 'Mid-Range'
    else:
        return 'Luxury-Seeking'

# Apply the function to create a new column
data['customer_segment'] = data.apply(customer_segment, axis=1)

# Analyze customer segments based on reviews and neighborhoods
segmentation_summary = data.groupby('customer_segment').agg({
    'price': 'mean',
    'number_of_reviews': 'mean',
    'neighbourhood_group': lambda x: x.mode()[0]  # Most common neighborhood group
}).reset_index()

print(segmentation_summary)

# Visualize the segmentation
plt.figure(figsize=(10, 6))
sns.boxplot(data=data, x='customer_segment', y='price', palette='viridis')
plt.title('Price Distribution Across Customer Segments', fontsize=16)
plt.xlabel('Customer Segment', fontsize=14)
plt.ylabel('Price', fontsize=14)
plt.grid(visible=True, linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

   customer_segment       price  number_of_reviews neighbourhood_group
0  Budget-Conscious   66.539350          41.806987            Brooklyn
1    Luxury-Seeking  337.304636          30.572185           Manhattan
2         Mid-Range  167.659678          45.667140           Manhattan

C:\Users\welcome\AppData\Local\Temp\ipykernel_8952\676959346.py:24: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(data=data, x='customer_segment', y='price', palette='viridis')

# Create a column categorizing hosts based on reviews
def host_reputation(row):
    if row['number_of_reviews'] > 50:
        return 'Highly Reviewed'
    elif 10 <= row['number_of_reviews'] <= 50:
        return 'Moderately Reviewed'
    else:
        return 'Lowly Reviewed'

data['host_reputation'] = data.apply(host_reputation, axis=1)

# Analyze host reputation
host_reputation_summary = data.groupby('host_reputation').agg({
    'number_of_reviews': 'mean',
    'reviews_per_month': 'mean',
    'price': 'mean'
}).reset_index()

print(host_reputation_summary)

# Visualize host reputation
plt.figure(figsize=(10, 6))
sns.barplot(data=host_reputation_summary, x='host_reputation', y='price', palette='coolwarm')
plt.title('Average Price Across Host Reputation Categories', fontsize=16)
plt.xlabel('Host Reputation', fontsize=14)
plt.ylabel('Average Price', fontsize=14)
plt.grid(visible=True, linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

       host_reputation  number_of_reviews  reviews_per_month       price
0      Highly Reviewed         134.388100           2.693772  134.994853
1       Lowly Reviewed           3.674211           0.435785  136.414309
2  Moderately Reviewed          24.818875           1.243042  135.838847

C:\Users\welcome\AppData\Local\Temp\ipykernel_8952\4196964096.py:23: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(data=host_reputation_summary, x='host_reputation', y='price', palette='coolwarm')

	id	name	host_id	host_name	neighbourhood_group	neighbourhood	latitude	longitude	room_type	price	...	last_review	reviews_per_month	calculated_host_listings_count	availability_365	number_of_reviews_ltm	license	rating	bedrooms	beds	baths
0	1.312228e+06	Rental unit in Brooklyn · ★5.0 · 1 bedroom	7130382	Walter	Brooklyn	Clinton Hill	40.683710	-73.964610	Private room	55.0	...	20/12/15	0.03	1.0	0.0	0.0	No License	5	1	1	Not specified
1	4.527754e+07	Rental unit in New York · ★4.67 · 2 bedrooms ·...	51501835	Jeniffer	Manhattan	Hell's Kitchen	40.766610	-73.988100	Entire home/apt	144.0	...	01/05/23	0.24	139.0	364.0	2.0	No License	4.67	2	1	1
2	9.710000e+17	Rental unit in New York · ★4.17 · 1 bedroom · ...	528871354	Joshua	Manhattan	Chelsea	40.750764	-73.994605	Entire home/apt	187.0	...	18/12/23	1.67	1.0	343.0	6.0	Exempt	4.17	1	2	1
3	3.857863e+06	Rental unit in New York · ★4.64 · 1 bedroom · ...	19902271	John And Catherine	Manhattan	Washington Heights	40.835600	-73.942500	Private room	120.0	...	17/09/23	1.38	2.0	363.0	12.0	No License	4.64	1	1	1
4	4.089661e+07	Condo in New York · ★4.91 · Studio · 1 bed · 1...	61391963	Stay With Vibe	Manhattan	Murray Hill	40.751120	-73.978600	Entire home/apt	85.0	...	03/12/23	0.24	133.0	335.0	3.0	No License	4.91	Studio	1	1

	id	host_id	latitude	longitude	price	minimum_nights	number_of_reviews	reviews_per_month	calculated_host_listings_count	availability_365	number_of_reviews_ltm	beds
count	2.077000e+04	2.077000e+04	20763.000000	20763.000000	20736.000000	20763.000000	20763.000000	20763.000000	20763.000000	20763.000000	20763.000000	20770.000000
mean	3.033858e+17	1.749049e+08	40.726821	-73.939179	187.714940	28.558493	42.610605	1.257589	18.866686	206.067957	10.848962	1.723592
std	3.901221e+17	1.725657e+08	0.060293	0.061403	1023.245124	33.532697	73.523401	1.904472	70.921443	135.077259	21.354876	1.211993
min	2.595000e+03	1.678000e+03	40.500314	-74.249840	10.000000	1.000000	1.000000	0.010000	1.000000	0.000000	0.000000	1.000000
25%	2.707260e+07	2.041184e+07	40.684159	-73.980755	80.000000	30.000000	4.000000	0.210000	1.000000	87.000000	1.000000	1.000000
50%	4.992852e+07	1.086990e+08	40.722890	-73.949597	125.000000	30.000000	14.000000	0.650000	2.000000	215.000000	3.000000	1.000000
75%	7.220000e+17	3.143997e+08	40.763106	-73.917475	199.000000	30.000000	49.000000	1.800000	5.000000	353.000000	15.000000	2.000000
max	1.050000e+18	5.504035e+08	40.911147	-73.713650	100000.000000	1250.000000	1865.000000	75.490000	713.000000	365.000000	1075.000000	42.000000

	id	name	host_id	host_name	neighbourhood_group	neighbourhood	latitude	longitude	room_type	price	...	last_review	reviews_per_month	calculated_host_listings_count	availability_365	number_of_reviews_ltm	license	rating	bedrooms	beds	baths
15913	47047419.0	Guest suite in New York · ★4.88 · 8 bedrooms ·...	380290574	Karen	Manhattan	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	OSE-STRREG-0000018	4.88	8	9	7
15914	38906302.0	Loft in Brooklyn · ★4.71 · 1 bedroom · 1 bed ·...	51954926	K	Brooklyn	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	No License	4.71	1	1	2

	id	name	host_id	host_name	neighbourhood_group	neighbourhood	latitude	longitude	room_type	price	...	last_review	reviews_per_month	calculated_host_listings_count	availability_365	number_of_reviews_ltm	license	rating	bedrooms	beds	baths
6	4.527754e+07	Rental unit in New York · ★4.67 · 2 bedrooms ·...	51501835	Jeniffer	Manhattan	Hell's Kitchen	40.766610	-73.988100	Entire home/apt	144.0	...	01/05/23	0.24	139.0	364.0	2.0	No License	4.67	2	1	1
7	9.710000e+17	Rental unit in New York · ★4.17 · 1 bedroom · ...	528871354	Joshua	Manhattan	Chelsea	40.750764	-73.994605	Entire home/apt	187.0	...	18/12/23	1.67	1.0	343.0	6.0	Exempt	4.17	1	2	1
8	3.857863e+06	Rental unit in New York · ★4.64 · 1 bedroom · ...	19902271	John And Catherine	Manhattan	Washington Heights	40.835600	-73.942500	Private room	120.0	...	17/09/23	1.38	2.0	363.0	12.0	No License	4.64	1	1	1
9	4.089661e+07	Condo in New York · ★4.91 · Studio · 1 bed · 1...	61391963	Stay With Vibe	Manhattan	Murray Hill	40.751120	-73.978600	Entire home/apt	85.0	...	03/12/23	0.24	133.0	335.0	3.0	No License	4.91	Studio	1	1
10	4.958498e+07	Rental unit in New York · ★5.0 · 1 bedroom · 1...	51501835	Jeniffer	Manhattan	Hell's Kitchen	40.759950	-73.992960	Entire home/apt	115.0	...	29/07/23	0.16	139.0	276.0	2.0	No License	5	1	1	1
20736	7.990000e+17	Rental unit in New York · 2 bedrooms · 2 beds ...	224733902	CozySuites Copake	Manhattan	Upper East Side	40.768970	-73.957592	Entire home/apt	153.0	...	15/09/23	0.41	8.0	308.0	2.0	No License	No rating	2	2	2
20737	5.930000e+17	Rental unit in New York · ★4.79 · 2 bedrooms ·...	23219783	Rob	Manhattan	West Village	40.730220	-74.002910	Entire home/apt	175.0	...	22/11/23	2.03	4.0	129.0	25.0	No License	4.79	2	2	1
20738	9.230000e+17	Loft in New York · ★4.33 · 1 bedroom · 2 beds ...	520265731	Rodrigo	Manhattan	Greenwich Village	40.728390	-73.999540	Entire home/apt	156.0	...	02/01/24	2.60	1.0	356.0	9.0	Exempt	4.33	1	2	1
20739	1.336161e+07	Rental unit in New York · ★4.89 · 2 bedrooms ·...	8961407	Jamie	Manhattan	Harlem	40.805700	-73.946250	Entire home/apt	397.0	...	08/09/23	1.08	3.0	274.0	3.0	No License	4.89	2	2	1
20740	5.119566e+07	Rental unit in New York · Studio · 1 bed · 1 bath	51501835	Jeniffer	Manhattan	Chinatown	40.718360	-73.995850	Entire home/apt	100.0	...	25/05/23	0.08	139.0	306.0	1.0	No License	No rating	Studio	1	1
20741	2.523473e+07	Rental unit in New York · ★4.41 · 1 bedroom · ...	1497427	Mara	Manhattan	Upper East Side	40.774030	-73.950580	Entire home/apt	120.0	...	31/03/23	0.26	2.0	364.0	1.0	No License	4.41	1	2	1
20742	3.339399e+06	Rental unit in New York · ★4.73 · 1 bedroom · ...	2119276	Urban Furnished	Manhattan	West Village	40.732030	-74.006760	Entire home/apt	143.0	...	09/05/23	0.20	54.0	285.0	2.0	No License	4.73	1	1	1

	id	name	host_id	host_name	neighbourhood_group	neighbourhood	latitude	longitude	room_type	price	...	reviews_per_month	calculated_host_listings_count	availability_365	number_of_reviews_ltm	license	rating	bedrooms	beds	baths	price per bed
0	1.312228e+06	Rental unit in Brooklyn · ★5.0 · 1 bedroom	7130382	Walter	Brooklyn	Clinton Hill	40.683710	-73.964610	Private room	55.0	...	0.03	1.0	0.0	0.0	No License	5	1	1	Not specified	55.0
1	4.527754e+07	Rental unit in New York · ★4.67 · 2 bedrooms ·...	51501835	Jeniffer	Manhattan	Hell's Kitchen	40.766610	-73.988100	Entire home/apt	144.0	...	0.24	139.0	364.0	2.0	No License	4.67	2	1	1	144.0
2	9.710000e+17	Rental unit in New York · ★4.17 · 1 bedroom · ...	528871354	Joshua	Manhattan	Chelsea	40.750764	-73.994605	Entire home/apt	187.0	...	1.67	1.0	343.0	6.0	Exempt	4.17	1	2	1	93.5
3	3.857863e+06	Rental unit in New York · ★4.64 · 1 bedroom · ...	19902271	John And Catherine	Manhattan	Washington Heights	40.835600	-73.942500	Private room	120.0	...	1.38	2.0	363.0	12.0	No License	4.64	1	1	1	120.0
4	4.089661e+07	Condo in New York · ★4.91 · Studio · 1 bed · 1...	61391963	Stay With Vibe	Manhattan	Murray Hill	40.751120	-73.978600	Entire home/apt	85.0	...	0.24	133.0	335.0	3.0	No License	4.91	Studio	1	1	85.0